{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-04-25T17:31:23-06:00\n",
      "\n",
      "CPython 3.6.0\n",
      "IPython 5.1.0\n",
      "\n",
      "compiler   : GCC 4.4.7 20120313 (Red Hat 4.4.7-1)\n",
      "system     : Linux\n",
      "release    : 4.4.0-72-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n",
      "\n",
      "pandas 0.19.2\n",
      "pandas_datareader 0.3.0.post\n",
      "numpy 1.12.1\n",
      "scipy 0.19.0\n",
      "sklearn 0.18.1\n",
      "matplotlib 2.0.0\n",
      "seaborn 0.7.1\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark\n",
    "from copy import deepcopy\n",
    "import pickle\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "from pandas.tseries.offsets import Week\n",
    "import numpy as np\n",
    "import sklearn.mixture as mix\n",
    "from sklearn.externals import joblib\n",
    "import scipy.stats as scs\n",
    "from math import ceil\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.25)\n",
    "style_kwds = {'xtick.major.size': 3, 'ytick.major.size': 3,\n",
    "              'font.family':'Courier Prime Code', 'legend.frameon': True} # \n",
    "sns.set_style('white', style_kwds)\n",
    "\n",
    "import missingno as msno\n",
    "from tqdm import tqdm\n",
    "import affirm \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import re\n",
    "p=print\n",
    "p()\n",
    "%watermark -p pandas,pandas_datareader,numpy,scipy,sklearn,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_hdf('GMM_Results_TidyData.h5', 'table')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <th colspan=\"10\" halign=\"left\">2</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>steps</th>\n",
       "      <th colspan=\"3\" halign=\"left\">1</th>\n",
       "      <th colspan=\"3\" halign=\"left\">2</th>\n",
       "      <th colspan=\"3\" halign=\"left\">3</th>\n",
       "      <th>5</th>\n",
       "      <th>...</th>\n",
       "      <th>5</th>\n",
       "      <th colspan=\"3\" halign=\"left\">7</th>\n",
       "      <th colspan=\"3\" halign=\"left\">10</th>\n",
       "      <th colspan=\"3\" halign=\"left\">21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lookback</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>999</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>999</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>999</th>\n",
       "      <th>1</th>\n",
       "      <th>...</th>\n",
       "      <th>999</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>999</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>999</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.72406</td>\n",
       "      <td>0.751246</td>\n",
       "      <td>0.628002</td>\n",
       "      <td>0.72406</td>\n",
       "      <td>0.751246</td>\n",
       "      <td>0.628002</td>\n",
       "      <td>0.72406</td>\n",
       "      <td>0.751246</td>\n",
       "      <td>0.628002</td>\n",
       "      <td>0.72406</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681921</td>\n",
       "      <td>0.435433</td>\n",
       "      <td>0.599909</td>\n",
       "      <td>0.681921</td>\n",
       "      <td>0.435433</td>\n",
       "      <td>0.599909</td>\n",
       "      <td>0.681921</td>\n",
       "      <td>0.435433</td>\n",
       "      <td>0.599909</td>\n",
       "      <td>0.681921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.00130</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.00060</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.00120</td>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.00330</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.004400</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.004500</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>0.001700</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.00130</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.00070</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.00310</td>\n",
       "      <td>0.003700</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.00640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.004100</td>\n",
       "      <td>0.005200</td>\n",
       "      <td>0.004800</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.007200</td>\n",
       "      <td>0.011400</td>\n",
       "      <td>0.009500</td>\n",
       "      <td>0.013300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_</th>\n",
       "      <td>0.04550</td>\n",
       "      <td>0.052500</td>\n",
       "      <td>0.060500</td>\n",
       "      <td>0.06820</td>\n",
       "      <td>0.119500</td>\n",
       "      <td>0.119500</td>\n",
       "      <td>0.09320</td>\n",
       "      <td>0.126900</td>\n",
       "      <td>0.126900</td>\n",
       "      <td>0.07180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107800</td>\n",
       "      <td>0.122400</td>\n",
       "      <td>0.142900</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.153100</td>\n",
       "      <td>0.153100</td>\n",
       "      <td>0.195400</td>\n",
       "      <td>0.173800</td>\n",
       "      <td>0.151200</td>\n",
       "      <td>0.188400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_</th>\n",
       "      <td>-0.04580</td>\n",
       "      <td>-0.077100</td>\n",
       "      <td>-0.077100</td>\n",
       "      <td>-0.07360</td>\n",
       "      <td>-0.097900</td>\n",
       "      <td>-0.097900</td>\n",
       "      <td>-0.14370</td>\n",
       "      <td>-0.143700</td>\n",
       "      <td>-0.143700</td>\n",
       "      <td>-0.18320</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.209600</td>\n",
       "      <td>-0.189100</td>\n",
       "      <td>-0.249100</td>\n",
       "      <td>-0.271100</td>\n",
       "      <td>-0.311500</td>\n",
       "      <td>-0.317900</td>\n",
       "      <td>-0.348800</td>\n",
       "      <td>-0.427000</td>\n",
       "      <td>-0.427000</td>\n",
       "      <td>-0.418000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gt0_cnt</th>\n",
       "      <td>118.00000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>108.00000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>130.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lt0_cnt</th>\n",
       "      <td>89.00000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>85.00000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>77.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum_gt0</th>\n",
       "      <td>1.01060</td>\n",
       "      <td>1.185000</td>\n",
       "      <td>1.761400</td>\n",
       "      <td>1.35450</td>\n",
       "      <td>1.732300</td>\n",
       "      <td>2.472000</td>\n",
       "      <td>1.80610</td>\n",
       "      <td>2.072300</td>\n",
       "      <td>2.998200</td>\n",
       "      <td>2.31640</td>\n",
       "      <td>...</td>\n",
       "      <td>2.959200</td>\n",
       "      <td>6.005200</td>\n",
       "      <td>4.607100</td>\n",
       "      <td>3.430500</td>\n",
       "      <td>7.510500</td>\n",
       "      <td>5.393200</td>\n",
       "      <td>4.055300</td>\n",
       "      <td>11.036800</td>\n",
       "      <td>7.086000</td>\n",
       "      <td>5.900800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum_lt0</th>\n",
       "      <td>-0.73200</td>\n",
       "      <td>-0.975700</td>\n",
       "      <td>-1.475400</td>\n",
       "      <td>-1.22740</td>\n",
       "      <td>-1.439600</td>\n",
       "      <td>-2.297600</td>\n",
       "      <td>-1.55860</td>\n",
       "      <td>-1.795600</td>\n",
       "      <td>-2.945000</td>\n",
       "      <td>-1.62370</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.476800</td>\n",
       "      <td>-5.058400</td>\n",
       "      <td>-3.029000</td>\n",
       "      <td>-2.944600</td>\n",
       "      <td>-6.281200</td>\n",
       "      <td>-3.800000</td>\n",
       "      <td>-3.572800</td>\n",
       "      <td>-9.167500</td>\n",
       "      <td>-6.470100</td>\n",
       "      <td>-5.639900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum_ratio</th>\n",
       "      <td>1.38060</td>\n",
       "      <td>1.214600</td>\n",
       "      <td>1.193900</td>\n",
       "      <td>1.10350</td>\n",
       "      <td>1.203300</td>\n",
       "      <td>1.075900</td>\n",
       "      <td>1.15880</td>\n",
       "      <td>1.154100</td>\n",
       "      <td>1.018100</td>\n",
       "      <td>1.42660</td>\n",
       "      <td>...</td>\n",
       "      <td>1.194800</td>\n",
       "      <td>1.187200</td>\n",
       "      <td>1.521000</td>\n",
       "      <td>1.165000</td>\n",
       "      <td>1.195700</td>\n",
       "      <td>1.419300</td>\n",
       "      <td>1.135100</td>\n",
       "      <td>1.203900</td>\n",
       "      <td>1.095200</td>\n",
       "      <td>1.046200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gt_pct</th>\n",
       "      <td>0.57000</td>\n",
       "      <td>0.558400</td>\n",
       "      <td>0.559700</td>\n",
       "      <td>0.52170</td>\n",
       "      <td>0.553300</td>\n",
       "      <td>0.528300</td>\n",
       "      <td>0.58940</td>\n",
       "      <td>0.599000</td>\n",
       "      <td>0.562900</td>\n",
       "      <td>0.62800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.596900</td>\n",
       "      <td>0.587800</td>\n",
       "      <td>0.595500</td>\n",
       "      <td>0.558100</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.598300</td>\n",
       "      <td>0.589100</td>\n",
       "      <td>0.615700</td>\n",
       "      <td>0.592700</td>\n",
       "      <td>0.600800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lt_pct</th>\n",
       "      <td>0.43000</td>\n",
       "      <td>0.441600</td>\n",
       "      <td>0.440300</td>\n",
       "      <td>0.47830</td>\n",
       "      <td>0.446700</td>\n",
       "      <td>0.471700</td>\n",
       "      <td>0.41060</td>\n",
       "      <td>0.401000</td>\n",
       "      <td>0.437100</td>\n",
       "      <td>0.37200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.403100</td>\n",
       "      <td>0.412200</td>\n",
       "      <td>0.404500</td>\n",
       "      <td>0.441900</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.401700</td>\n",
       "      <td>0.410900</td>\n",
       "      <td>0.384300</td>\n",
       "      <td>0.407300</td>\n",
       "      <td>0.399200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 168 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "k                 2                                                  \\\n",
       "steps             1                                  2                \n",
       "lookback         1           3           999        1           3     \n",
       "accuracy     0.72406    0.751246    0.628002    0.72406    0.751246   \n",
       "mean         0.00130    0.001100    0.000900    0.00060    0.001500   \n",
       "median       0.00130    0.002000    0.001300    0.00070    0.002100   \n",
       "max_         0.04550    0.052500    0.060500    0.06820    0.119500   \n",
       "min_        -0.04580   -0.077100   -0.077100   -0.07360   -0.097900   \n",
       "gt0_cnt    118.00000  110.000000  178.000000  108.00000  109.000000   \n",
       "lt0_cnt     89.00000   87.000000  140.000000   99.00000   88.000000   \n",
       "sum_gt0      1.01060    1.185000    1.761400    1.35450    1.732300   \n",
       "sum_lt0     -0.73200   -0.975700   -1.475400   -1.22740   -1.439600   \n",
       "sum_ratio    1.38060    1.214600    1.193900    1.10350    1.203300   \n",
       "gt_pct       0.57000    0.558400    0.559700    0.52170    0.553300   \n",
       "lt_pct       0.43000    0.441600    0.440300    0.47830    0.446700   \n",
       "\n",
       "k                                                                    \\\n",
       "steps                         3                                  5    \n",
       "lookback          999        1           3           999        1     \n",
       "accuracy     0.628002    0.72406    0.751246    0.628002    0.72406   \n",
       "mean         0.000500    0.00120    0.001400    0.000200    0.00330   \n",
       "median       0.000800    0.00310    0.003700    0.002800    0.00640   \n",
       "max_         0.119500    0.09320    0.126900    0.126900    0.07180   \n",
       "min_        -0.097900   -0.14370   -0.143700   -0.143700   -0.18320   \n",
       "gt0_cnt    168.000000  122.00000  118.000000  179.000000  130.00000   \n",
       "lt0_cnt    150.000000   85.00000   79.000000  139.000000   77.00000   \n",
       "sum_gt0      2.472000    1.80610    2.072300    2.998200    2.31640   \n",
       "sum_lt0     -2.297600   -1.55860   -1.795600   -2.945000   -1.62370   \n",
       "sum_ratio    1.075900    1.15880    1.154100    1.018100    1.42660   \n",
       "gt_pct       0.528300    0.58940    0.599000    0.562900    0.62800   \n",
       "lt_pct       0.471700    0.41060    0.401000    0.437100    0.37200   \n",
       "\n",
       "k             ...              21                                      \\\n",
       "steps         ...              5           7                            \n",
       "lookback      ...             999         1           3           999   \n",
       "accuracy      ...        0.681921    0.435433    0.599909    0.681921   \n",
       "mean          ...        0.001900    0.001600    0.004400    0.001900   \n",
       "median        ...        0.005000    0.004100    0.005200    0.004800   \n",
       "max_          ...        0.107800    0.122400    0.142900    0.160000   \n",
       "min_          ...       -0.209600   -0.189100   -0.249100   -0.271100   \n",
       "gt0_cnt       ...      154.000000  338.000000  212.000000  144.000000   \n",
       "lt0_cnt       ...      104.000000  237.000000  144.000000  114.000000   \n",
       "sum_gt0       ...        2.959200    6.005200    4.607100    3.430500   \n",
       "sum_lt0       ...       -2.476800   -5.058400   -3.029000   -2.944600   \n",
       "sum_ratio     ...        1.194800    1.187200    1.521000    1.165000   \n",
       "gt_pct        ...        0.596900    0.587800    0.595500    0.558100   \n",
       "lt_pct        ...        0.403100    0.412200    0.404500    0.441900   \n",
       "\n",
       "k                                                                      \\\n",
       "steps              10                                  21               \n",
       "lookback          1           3           999         1           3     \n",
       "accuracy     0.435433    0.599909    0.681921    0.435433    0.599909   \n",
       "mean         0.002100    0.004500    0.001900    0.003300    0.001700   \n",
       "median       0.006000    0.008000    0.007200    0.011400    0.009500   \n",
       "max_         0.153100    0.153100    0.195400    0.173800    0.151200   \n",
       "min_        -0.311500   -0.317900   -0.348800   -0.427000   -0.427000   \n",
       "gt0_cnt    345.000000  213.000000  152.000000  354.000000  211.000000   \n",
       "lt0_cnt    230.000000  143.000000  106.000000  221.000000  145.000000   \n",
       "sum_gt0      7.510500    5.393200    4.055300   11.036800    7.086000   \n",
       "sum_lt0     -6.281200   -3.800000   -3.572800   -9.167500   -6.470100   \n",
       "sum_ratio    1.195700    1.419300    1.135100    1.203900    1.095200   \n",
       "gt_pct       0.600000    0.598300    0.589100    0.615700    0.592700   \n",
       "lt_pct       0.400000    0.401700    0.410900    0.384300    0.407300   \n",
       "\n",
       "k                      \n",
       "steps                  \n",
       "lookback          999  \n",
       "accuracy     0.681921  \n",
       "mean         0.001000  \n",
       "median       0.013300  \n",
       "max_         0.188400  \n",
       "min_        -0.418000  \n",
       "gt0_cnt    155.000000  \n",
       "lt0_cnt    103.000000  \n",
       "sum_gt0      5.900800  \n",
       "sum_lt0     -5.639900  \n",
       "sum_ratio    1.046200  \n",
       "gt_pct       0.600800  \n",
       "lt_pct       0.399200  \n",
       "\n",
       "[12 rows x 168 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can view individual results\n",
    "df.query('sym == \"SPY\"').groupby(['k', 'steps','lookback']).mean().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lookback</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>999</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <th>steps</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2</th>\n",
       "      <th>1</th>\n",
       "      <td>1.3806</td>\n",
       "      <td>1.2146</td>\n",
       "      <td>1.1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1035</td>\n",
       "      <td>1.2033</td>\n",
       "      <td>1.0759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1588</td>\n",
       "      <td>1.1541</td>\n",
       "      <td>1.0181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.4266</td>\n",
       "      <td>1.2988</td>\n",
       "      <td>1.2355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.3061</td>\n",
       "      <td>1.2181</td>\n",
       "      <td>1.1933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.2528</td>\n",
       "      <td>1.1140</td>\n",
       "      <td>1.1013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.1795</td>\n",
       "      <td>0.9939</td>\n",
       "      <td>1.0623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">3</th>\n",
       "      <th>1</th>\n",
       "      <td>1.5326</td>\n",
       "      <td>1.3799</td>\n",
       "      <td>1.1629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.2588</td>\n",
       "      <td>1.0730</td>\n",
       "      <td>0.9559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.2554</td>\n",
       "      <td>1.0362</td>\n",
       "      <td>0.9248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.4943</td>\n",
       "      <td>1.1174</td>\n",
       "      <td>1.0305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.3562</td>\n",
       "      <td>1.1055</td>\n",
       "      <td>1.0608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.3104</td>\n",
       "      <td>1.0478</td>\n",
       "      <td>0.9581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.1694</td>\n",
       "      <td>1.0987</td>\n",
       "      <td>1.0401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">5</th>\n",
       "      <th>1</th>\n",
       "      <td>1.2299</td>\n",
       "      <td>1.3337</td>\n",
       "      <td>1.1743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1551</td>\n",
       "      <td>1.0618</td>\n",
       "      <td>0.9798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1248</td>\n",
       "      <td>1.1060</td>\n",
       "      <td>0.9332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.4150</td>\n",
       "      <td>1.4754</td>\n",
       "      <td>1.1586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.3135</td>\n",
       "      <td>1.4061</td>\n",
       "      <td>1.2162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.1943</td>\n",
       "      <td>1.3099</td>\n",
       "      <td>1.0387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0959</td>\n",
       "      <td>1.1919</td>\n",
       "      <td>0.8289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">7</th>\n",
       "      <th>1</th>\n",
       "      <td>1.0653</td>\n",
       "      <td>1.5026</td>\n",
       "      <td>1.2731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0596</td>\n",
       "      <td>1.0715</td>\n",
       "      <td>1.0788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1546</td>\n",
       "      <td>1.0525</td>\n",
       "      <td>1.1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.4114</td>\n",
       "      <td>1.4787</td>\n",
       "      <td>1.3386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.2811</td>\n",
       "      <td>1.4847</td>\n",
       "      <td>1.2621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.2368</td>\n",
       "      <td>1.3933</td>\n",
       "      <td>1.2419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.3120</td>\n",
       "      <td>1.1530</td>\n",
       "      <td>1.0946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">9</th>\n",
       "      <th>1</th>\n",
       "      <td>1.0479</td>\n",
       "      <td>1.3456</td>\n",
       "      <td>1.3508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0196</td>\n",
       "      <td>1.0609</td>\n",
       "      <td>1.1059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1100</td>\n",
       "      <td>1.0403</td>\n",
       "      <td>1.1713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.3666</td>\n",
       "      <td>1.2846</td>\n",
       "      <td>1.4543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.3124</td>\n",
       "      <td>1.1804</td>\n",
       "      <td>1.4201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.2260</td>\n",
       "      <td>1.1591</td>\n",
       "      <td>1.2543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.2077</td>\n",
       "      <td>1.0994</td>\n",
       "      <td>1.2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">13</th>\n",
       "      <th>1</th>\n",
       "      <td>1.1263</td>\n",
       "      <td>1.1889</td>\n",
       "      <td>1.4211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0648</td>\n",
       "      <td>1.0269</td>\n",
       "      <td>1.1443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1574</td>\n",
       "      <td>1.0529</td>\n",
       "      <td>1.2717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.3378</td>\n",
       "      <td>1.2382</td>\n",
       "      <td>1.3161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.2053</td>\n",
       "      <td>1.1437</td>\n",
       "      <td>1.2919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.1588</td>\n",
       "      <td>1.1268</td>\n",
       "      <td>1.1386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.3624</td>\n",
       "      <td>1.0367</td>\n",
       "      <td>0.9793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">17</th>\n",
       "      <th>1</th>\n",
       "      <td>1.2072</td>\n",
       "      <td>1.2717</td>\n",
       "      <td>1.1266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.2261</td>\n",
       "      <td>1.1880</td>\n",
       "      <td>0.8662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1569</td>\n",
       "      <td>1.2243</td>\n",
       "      <td>0.9143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.2874</td>\n",
       "      <td>1.2782</td>\n",
       "      <td>1.1725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.3338</td>\n",
       "      <td>1.0865</td>\n",
       "      <td>1.0402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.3186</td>\n",
       "      <td>1.1162</td>\n",
       "      <td>0.9493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.3412</td>\n",
       "      <td>1.0565</td>\n",
       "      <td>0.7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">21</th>\n",
       "      <th>1</th>\n",
       "      <td>1.1376</td>\n",
       "      <td>1.4016</td>\n",
       "      <td>1.3197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1715</td>\n",
       "      <td>1.3162</td>\n",
       "      <td>1.0202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1335</td>\n",
       "      <td>1.3548</td>\n",
       "      <td>1.0144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.2000</td>\n",
       "      <td>1.5998</td>\n",
       "      <td>1.1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.1872</td>\n",
       "      <td>1.5210</td>\n",
       "      <td>1.1650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.1957</td>\n",
       "      <td>1.4193</td>\n",
       "      <td>1.1351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.2039</td>\n",
       "      <td>1.0952</td>\n",
       "      <td>1.0462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lookback     1       3       999\n",
       "k  steps                        \n",
       "2  1      1.3806  1.2146  1.1939\n",
       "   2      1.1035  1.2033  1.0759\n",
       "   3      1.1588  1.1541  1.0181\n",
       "   5      1.4266  1.2988  1.2355\n",
       "   7      1.3061  1.2181  1.1933\n",
       "   10     1.2528  1.1140  1.1013\n",
       "   21     1.1795  0.9939  1.0623\n",
       "3  1      1.5326  1.3799  1.1629\n",
       "   2      1.2588  1.0730  0.9559\n",
       "   3      1.2554  1.0362  0.9248\n",
       "   5      1.4943  1.1174  1.0305\n",
       "   7      1.3562  1.1055  1.0608\n",
       "   10     1.3104  1.0478  0.9581\n",
       "   21     1.1694  1.0987  1.0401\n",
       "5  1      1.2299  1.3337  1.1743\n",
       "   2      1.1551  1.0618  0.9798\n",
       "   3      1.1248  1.1060  0.9332\n",
       "   5      1.4150  1.4754  1.1586\n",
       "   7      1.3135  1.4061  1.2162\n",
       "   10     1.1943  1.3099  1.0387\n",
       "   21     1.0959  1.1919  0.8289\n",
       "7  1      1.0653  1.5026  1.2731\n",
       "   2      1.0596  1.0715  1.0788\n",
       "   3      1.1546  1.0525  1.1063\n",
       "   5      1.4114  1.4787  1.3386\n",
       "   7      1.2811  1.4847  1.2621\n",
       "   10     1.2368  1.3933  1.2419\n",
       "   21     1.3120  1.1530  1.0946\n",
       "9  1      1.0479  1.3456  1.3508\n",
       "   2      1.0196  1.0609  1.1059\n",
       "   3      1.1100  1.0403  1.1713\n",
       "   5      1.3666  1.2846  1.4543\n",
       "   7      1.3124  1.1804  1.4201\n",
       "   10     1.2260  1.1591  1.2543\n",
       "   21     1.2077  1.0994  1.2401\n",
       "13 1      1.1263  1.1889  1.4211\n",
       "   2      1.0648  1.0269  1.1443\n",
       "   3      1.1574  1.0529  1.2717\n",
       "   5      1.3378  1.2382  1.3161\n",
       "   7      1.2053  1.1437  1.2919\n",
       "   10     1.1588  1.1268  1.1386\n",
       "   21     1.3624  1.0367  0.9793\n",
       "17 1      1.2072  1.2717  1.1266\n",
       "   2      1.2261  1.1880  0.8662\n",
       "   3      1.1569  1.2243  0.9143\n",
       "   5      1.2874  1.2782  1.1725\n",
       "   7      1.3338  1.0865  1.0402\n",
       "   10     1.3186  1.1162  0.9493\n",
       "   21     1.3412  1.0565  0.7675\n",
       "21 1      1.1376  1.4016  1.3197\n",
       "   2      1.1715  1.3162  1.0202\n",
       "   3      1.1335  1.3548  1.0144\n",
       "   5      1.2000  1.5998  1.1948\n",
       "   7      1.1872  1.5210  1.1650\n",
       "   10     1.1957  1.4193  1.1351\n",
       "   21     1.2039  1.0952  1.0462"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('sym == \"SPY\"').groupby(['k', 'steps','lookback'])['sum_ratio'].mean().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sym\n",
       "EEM    0.000977\n",
       "EFA    0.002130\n",
       "GLD    0.000771\n",
       "QQQ    0.005810\n",
       "SPY    0.004829\n",
       "TLT    0.002437\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = df.groupby(['sym', 'lookback'])['median'].mean().unstack().mean(axis=1)\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>k</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>7</th>\n",
       "      <th>9</th>\n",
       "      <th>13</th>\n",
       "      <th>17</th>\n",
       "      <th>21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sym</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EEM</th>\n",
       "      <td>0.004195</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>-0.000805</td>\n",
       "      <td>0.000771</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EFA</th>\n",
       "      <td>0.003414</td>\n",
       "      <td>0.000990</td>\n",
       "      <td>0.002214</td>\n",
       "      <td>0.001810</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001990</td>\n",
       "      <td>0.001495</td>\n",
       "      <td>0.003129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLD</th>\n",
       "      <td>0.000843</td>\n",
       "      <td>0.000952</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>-0.000067</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>0.000795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QQQ</th>\n",
       "      <td>0.006552</td>\n",
       "      <td>0.006690</td>\n",
       "      <td>0.005933</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.005852</td>\n",
       "      <td>0.004971</td>\n",
       "      <td>0.005243</td>\n",
       "      <td>0.005233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>0.006505</td>\n",
       "      <td>0.005195</td>\n",
       "      <td>0.004957</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.004824</td>\n",
       "      <td>0.004262</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.004629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLT</th>\n",
       "      <td>0.003152</td>\n",
       "      <td>0.002310</td>\n",
       "      <td>0.001986</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.002724</td>\n",
       "      <td>0.002233</td>\n",
       "      <td>0.002805</td>\n",
       "      <td>0.002038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "k          2         3         5         7         9         13        17  \\\n",
       "sym                                                                         \n",
       "EEM  0.004195  0.000376  0.001681  0.001310 -0.000805  0.000771  0.000086   \n",
       "EFA  0.003414  0.000990  0.002214  0.001810  0.002000  0.001990  0.001495   \n",
       "GLD  0.000843  0.000952  0.000886 -0.000067  0.000867  0.001314  0.000581   \n",
       "QQQ  0.006552  0.006690  0.005933  0.006000  0.005852  0.004971  0.005243   \n",
       "SPY  0.006505  0.005195  0.004957  0.004757  0.004824  0.004262  0.003500   \n",
       "TLT  0.003152  0.002310  0.001986  0.002248  0.002724  0.002233  0.002805   \n",
       "\n",
       "k          21  \n",
       "sym            \n",
       "EEM  0.000205  \n",
       "EFA  0.003129  \n",
       "GLD  0.000795  \n",
       "QQQ  0.005233  \n",
       "SPY  0.004629  \n",
       "TLT  0.002038  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# median returns by K\n",
    "df1 = df.groupby(['sym', 'k'])['median'].mean().unstack()#.mean(axis=1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>steps</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>7</th>\n",
       "      <th>10</th>\n",
       "      <th>21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sym</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EEM</th>\n",
       "      <td>0.001596</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>-0.000592</td>\n",
       "      <td>0.002512</td>\n",
       "      <td>0.002221</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>-0.002875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EFA</th>\n",
       "      <td>0.000558</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>-0.000350</td>\n",
       "      <td>0.001842</td>\n",
       "      <td>0.005412</td>\n",
       "      <td>0.005479</td>\n",
       "      <td>0.001558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLD</th>\n",
       "      <td>0.001063</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>0.000546</td>\n",
       "      <td>-0.001042</td>\n",
       "      <td>-0.000538</td>\n",
       "      <td>0.003204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QQQ</th>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000937</td>\n",
       "      <td>0.003329</td>\n",
       "      <td>0.006446</td>\n",
       "      <td>0.005383</td>\n",
       "      <td>0.009713</td>\n",
       "      <td>0.014642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>0.001033</td>\n",
       "      <td>0.000675</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.005063</td>\n",
       "      <td>0.004925</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.012179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLT</th>\n",
       "      <td>0.001521</td>\n",
       "      <td>0.000921</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>0.002388</td>\n",
       "      <td>0.003367</td>\n",
       "      <td>0.002712</td>\n",
       "      <td>0.005329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "steps        1         2         3         5         7         10        21\n",
       "sym                                                                        \n",
       "EEM    0.001596  0.000096 -0.000592  0.002512  0.002221  0.003883 -0.002875\n",
       "EFA    0.000558  0.000412 -0.000350  0.001842  0.005412  0.005479  0.001558\n",
       "GLD    0.001063  0.000758  0.001408  0.000546 -0.001042 -0.000538  0.003204\n",
       "QQQ    0.000217  0.000937  0.003329  0.006446  0.005383  0.009713  0.014642\n",
       "SPY    0.001033  0.000675  0.002500  0.005063  0.004925  0.007425  0.012179\n",
       "TLT    0.001521  0.000921  0.000821  0.002388  0.003367  0.002712  0.005329"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# median returns by step\n",
    "df1 = df.groupby(['sym', 'steps'])['median'].mean().unstack()#.mean(axis=1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>steps</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>7</th>\n",
       "      <th>10</th>\n",
       "      <th>21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sym</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EEM</th>\n",
       "      <td>1.370908</td>\n",
       "      <td>1.069050</td>\n",
       "      <td>1.142558</td>\n",
       "      <td>1.218625</td>\n",
       "      <td>1.076463</td>\n",
       "      <td>0.993300</td>\n",
       "      <td>0.658767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EFA</th>\n",
       "      <td>1.261367</td>\n",
       "      <td>0.930817</td>\n",
       "      <td>0.925383</td>\n",
       "      <td>1.030763</td>\n",
       "      <td>1.040233</td>\n",
       "      <td>0.966746</td>\n",
       "      <td>0.670408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLD</th>\n",
       "      <td>1.090254</td>\n",
       "      <td>1.015225</td>\n",
       "      <td>1.031525</td>\n",
       "      <td>0.989417</td>\n",
       "      <td>1.011271</td>\n",
       "      <td>1.036229</td>\n",
       "      <td>1.160317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QQQ</th>\n",
       "      <td>1.141629</td>\n",
       "      <td>1.113429</td>\n",
       "      <td>1.191242</td>\n",
       "      <td>1.367733</td>\n",
       "      <td>1.317471</td>\n",
       "      <td>1.274929</td>\n",
       "      <td>1.155454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>1.266183</td>\n",
       "      <td>1.095317</td>\n",
       "      <td>1.109442</td>\n",
       "      <td>1.317129</td>\n",
       "      <td>1.253800</td>\n",
       "      <td>1.183212</td>\n",
       "      <td>1.110679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLT</th>\n",
       "      <td>1.255213</td>\n",
       "      <td>1.123088</td>\n",
       "      <td>1.160733</td>\n",
       "      <td>1.350704</td>\n",
       "      <td>1.349554</td>\n",
       "      <td>1.461875</td>\n",
       "      <td>1.469208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "steps        1         2         3         5         7         10        21\n",
       "sym                                                                        \n",
       "EEM    1.370908  1.069050  1.142558  1.218625  1.076463  0.993300  0.658767\n",
       "EFA    1.261367  0.930817  0.925383  1.030763  1.040233  0.966746  0.670408\n",
       "GLD    1.090254  1.015225  1.031525  0.989417  1.011271  1.036229  1.160317\n",
       "QQQ    1.141629  1.113429  1.191242  1.367733  1.317471  1.274929  1.155454\n",
       "SPY    1.266183  1.095317  1.109442  1.317129  1.253800  1.183212  1.110679\n",
       "TLT    1.255213  1.123088  1.160733  1.350704  1.349554  1.461875  1.469208"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum ratio by step\n",
    "df2 = df.groupby(['sym', 'steps'])['sum_ratio'].mean().unstack()#.mean(axis=1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAGeCAYAAACaQ7f1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGAAAAKZCAwwAAABToQEGAACAqfx/1DlCJYoNu1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f068dbafd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fun with facet grids\n",
    "g = sns.FacetGrid(df, col=\"sym\", hue=\"lookback\", col_wrap=3)\n",
    "g.map(plt.hist, \"sum_ratio\", alpha=0.5);\n",
    "g.add_legend();\n",
    "for ax in g.axes.flat:\n",
    "    ax.axvline(1, color='k', lw=1, ls='--')\n",
    "g.fig.savefig('facetgrid-lookback-steps-sum-ratio-hist.png', dpi=300, bbox_inches='tight') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAGeCAYAAACaQ7f1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0688d726a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"sym\", hue=\"lookback\", col_wrap=3)\n",
    "g.map(plt.hist, \"median\", alpha=0.5);\n",
    "g.add_legend();\n",
    "for ax in g.axes.flat:\n",
    "    ax.axvline(0, color='k', lw=1, ls='--')\n",
    "g.fig.savefig('facetgrid-lookback-steps-median-return-hist.png', dpi=300, bbox_inches='tight')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArsAAAGdCAYAAAASfrs0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0689cbfb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"sym\", hue=\"lookback\", col_wrap=3)\n",
    "g.map(sns.barplot, \"steps\", \"median\", ci=None, alpha=0.5)\n",
    "g.add_legend();\n",
    "for ax in g.axes.flat:\n",
    "    ax.axhline(0, color='k', lw=1, ls='--')\n",
    "g.fig.savefig('facetgrid-lookback-steps-median-return-barplot.png', dpi=300, bbox_inches='tight')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr4AAAGeCAYAAABywwLeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0689853320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"sym\", hue=\"lookback\", col_wrap=3)\n",
    "g.map(sns.barplot, \"steps\", \"sum_ratio\", ci=None, alpha=0.5)\n",
    "g.add_legend();\n",
    "for ax in g.axes.flat:\n",
    "    ax.axhline(1., color='k', lw=1, ls='--')\n",
    "g.fig.savefig('facetgrid-lookback-steps-sum-ratio-barplot.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAGeCAYAAAB2NtLjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhRcAAACB9j+rNaut2VYB1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0689e0e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"sym\", hue=\"lookback\", col_wrap=3)\n",
    "g.map(sns.barplot, \"k\", \"median\", ci=None, alpha=0.5)\n",
    "g.add_legend();\n",
    "for ax in g.axes.flat:\n",
    "    ax.axhline(0, color='k', lw=1, ls='--')\n",
    "g.fig.savefig('facetgrid-lookback-k-median-return-barplot.png', dpi=300, bbox_inches='tight')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr4AAAGeCAYAAABywwLeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f068a1afa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"sym\", hue=\"lookback\", col_wrap=3)\n",
    "g.map(sns.barplot, \"k\", \"sum_ratio\", ci=None, alpha=0.5)\n",
    "g.add_legend();\n",
    "for ax in g.axes.flat:\n",
    "    ax.axhline(1., color='k', lw=1, ls='--')\n",
    "g.fig.savefig('facetgrid-lookback-k-sum-ratio-barplot.png', dpi=300, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}